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43 results for “multi-view”
ELKI Multi-View Clustering Data Sets Based on the Amsterdam Library of Object Images (ALOI)
<p>These data sets were originally created for the following publications:</p> <p><em>M. E. Houle, H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek</em><br> <strong>Can Shared-Neighbor Distances Defeat the Curse of Dimensionality?</strong><br> In Proceedings of the 22nd International Conference on Scientific and Statistical Database Management (SSDBM), Heidelberg, Germany, 2010.</p> <p><em>H.-P. Kriegel, E. Schubert, A. Zimek</em><br> <strong>Evaluation of Multiple Clustering Solutions</strong><br> In 2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with ECML PKDD 2011, Athens, Greece, 2011.</p> <p>The outlier data set versions were introduced in:</p> <p><em>E. Schubert, R. Wojdanowski, A. Zimek, H.-P. Kriegel</em><br> <strong>On Evaluation of Outlier Rankings and Outlier Scores</strong><br> In Proceedings of the 12th SIAM International Conference on Data Mining (SDM), Anaheim, CA, 2012.</p> <p> </p> <p>They are derived from the original image data available at <a href="https://aloi.science.uva.nl/">https://aloi.science.uva.nl/</a></p> <p>The image acquisition process is documented in the original ALOI work: <em>J. M. Geusebroek, G. J. Burghouts, and A. W. M. Smeulders</em>, <strong>The Amsterdam library of object images</strong>, Int. J. Comput. Vision, 61(1), 103-112, January, 2005</p> <p>Additional information is available at: <a href="https://elki-project.github.io/datasets/multi_view">https://elki-project.github.io/datasets/multi_view</a></p> <p>The following views are currently available:</p> <table> <tbody><tr> <th>Feature type</th> <th>Description</th> <th>Files</th> </tr> <tr> <td>Object number</td> <td>Sparse 1000 dimensional vectors that give the <em>true</em> object assignment</td> <td><a href="6355684/files/objs.arff.gz">objs.arff.gz</a></td> </tr> <tr> <td>RGB color histograms</td> <td>Standard RGB color histograms (uniform binning)</td> <td><a href="6355684/files/aloi-8d.csv.gz">aloi-8d.csv.gz</a> <a href="6355684/files/aloi-27d.csv.gz">aloi-27d.csv.gz</a> <a href="6355684/files/aloi-64d.csv.gz">aloi-64d.csv.gz</a> <a href="6355684/files/aloi-125d.csv.gz">aloi-125d.csv.gz</a> <a href="6355684/files/aloi-216d.csv.gz">aloi-216d.csv.gz</a> <a href="6355684/files/aloi-343d.csv.gz">aloi-343d.csv.gz</a> <a href="6355684/files/aloi-512d.csv.gz">aloi-512d.csv.gz</a> <a href="6355684/files/aloi-729d.csv.gz">aloi-729d.csv.gz</a> <a href="6355684/files/aloi-1000d.csv.gz">aloi-1000d.csv.gz</a></td> </tr> <tr> <td>HSV color histograms</td> <td>Standard HSV/HSB color histograms in various binnings</td> <td><a href="6355684/files/aloi-hsb-2x2x2.csv.gz">aloi-hsb-2x2x2.csv.gz</a> <a href="6355684/files/aloi-hsb-3x3x3.csv.gz">aloi-hsb-3x3x3.csv.gz</a> <a href="6355684/files/aloi-hsb-4x4x4.csv.gz">aloi-hsb-4x4x4.csv.gz</a> <a href="6355684/files/aloi-hsb-5x5x5.csv.gz">aloi-hsb-5x5x5.csv.gz</a> <a href="6355684/files/aloi-hsb-6x6x6.csv.gz">aloi-hsb-6x6x6.csv.gz</a> <a href="6355684/files/aloi-hsb-7x7x7.csv.gz">aloi-hsb-7x7x7.csv.gz</a> <a href="6355684/files/aloi-hsb-7x2x2.csv.gz">aloi-hsb-7x2x2.csv.gz</a> <a href="6355684/files/aloi-hsb-7x3x3.csv.gz">aloi-hsb-7x3x3.csv.gz</a> <a href="6355684/files/aloi-hsb-14x3x3.csv.gz">aloi-hsb-14x3x3.csv.gz</a> <a href="6355684/files/aloi-hsb-8x4x4.csv.gz">aloi-hsb-8x4x4.csv.gz</a> <a href="6355684/files/aloi-hsb-9x5x5.csv.gz">aloi-hsb-9x5x5.csv.gz</a> <a href="6355684/files/aloi-hsb-13x4x4.csv.gz">aloi-hsb-13x4x4.csv.gz</a> <a href="6355684/files/aloi-hsb-14x5x5.csv.gz">aloi-hsb-14x5x5.csv.gz</a> <a href="6355684/files/aloi-hsb-10x6x6.csv.gz">aloi-hsb-10x6x6.csv.gz</a> <a href="6355684/files/aloi-hsb-14x6x6.csv.gz">aloi-hsb-14x6x6.csv.gz</a></td> </tr> <tr> <td>Color similiarity</td> <td>Average similarity to 77 reference colors (not histograms) 18 colors x 2 sat x 2 bri + 5 grey values (incl. white, black)</td> <td><a href="6355684/files/aloi-colorsim77.arff.gz">aloi-colorsim77.arff.gz</a> (feature subsets are meaningful here, as these features are computed independently of each other)</td> </tr> <tr> <td>Haralick features</td> <td>First 13 Haralick features (radius 1 pixel)</td> <td><a href="6355684/files/aloi-haralick-1.csv.gz">aloi-haralick-1.csv.gz</a></td> </tr> <tr> <td>Front to back</td> <td>Vectors representing front face vs. back faces of individual objects</td> <td><a href="6355684/files/front.arff.gz">front.arff.gz</a></td> </tr> <tr> <td>Basic light</td> <td>Vectors indicating basic light situations</td> <td><a href="6355684/files/light.arff.gz">light.arff.gz</a></td> </tr> <tr> <td>Manual annotations</td> <td>Manually annotated object groups of semantically related objects such as cups</td> <td><a href="6355684/files/manual1.arff.gz">manual1.arff.gz</a></td> </tr> </tbody></table> <p><strong>Outlier Detection Versions</strong></p> <p>Additionally, we generated a number of subsets for outlier detection:</p> <table> <tbody><tr> <th>Feature type</th> <th>Description</th> <th>Files</th> </tr> <tr> <td>RGB Histograms</td> <td>Downsampled to 100000 objects (553 outliers)</td> <td><a href="6355684/files/aloi-27d-100000-max10-tot553.csv.gz">aloi-27d-100000-max10-tot553.csv.gz</a> <a href="6355684/files/aloi-64d-100000-max10-tot553.csv.gz">aloi-64d-100000-max10-tot553.csv.gz</a></td> </tr> <tr> <td> </td> <td>Downsampled to 75000 objects (717 outliers)</td> <td><a href="6355684/files/aloi-27d-75000-max4-tot717.csv.gz">aloi-27d-75000-max4-tot717.csv.gz</a> <a href="6355684/files/aloi-64d-75000-max4-tot717.csv.gz">aloi-64d-75000-max4-tot717.csv.gz</a></td> </tr> <tr> <td> </td> <td>Downsampled to 50000 objects (1508 outliers)</td> <td><a href="6355684/files/aloi-27d-50000-max5-tot1508.csv.gz">aloi-27d-50000-max5-tot1508.csv.gz</a> <a href="6355684/files/aloi-64d-50000-max5-tot1508.csv.gz">aloi-64d-50000-max5-tot1508.csv.gz</a></td> </tr> </tbody></table>
Multi-view rendered YCB dataset for mobile manipulation
<p>This dataset contains different scenarios wherein a mobile robot is approaching a set of YCB objects using both its base and arm motions. There are a total of sixteen sequences with around 100-time steps per sequence. All the sequences were generated using BlenderProc photo-realistic renderer (https://github.com/DLR-RM/BlenderProc). Eight different YCB objects were used. All these objects have a unique 6D pose, while some of the objects also have a single or multiple axes of symmetry. In each sequence maximum of three objects were randomly sampled. In addition, for each sequence, there are five views of the objects from external cameras placed between 2.5-3-5 m facing towards the objects. </p> <p>This dataset was used for experimental evaluation in the following<strong> </strong>ICRA 2022 paper:</p> <p><em><strong>Naik, L., Iversen, T. M., Kramberger, A., Wilm, J., & Krüger, N. (Accepted/In press). Multi-view object pose distribution tracking for pre-grasp planning on mobile robots. In 2022 IEEE International Conference on Robotics and Automation (ICRA) IEEE.</strong></em></p> <p> </p> <p><strong>Technical details:</strong></p> <p>In each sequence, the first 5 frames (0-4) contain views from external cameras while frames (5-104) provides a view of the objects visible in the robot camera as it approaches the objects. All the ground truths are provided using the 'coco' annotations format.</p>
Multi-view emotional expressions dataset
<p>Multi-view emotional expressions dataset (MEED) using 2D pose estimation.</p>
MVCNN++: CAD model shape classification and retrieval using multi-view convolutional neural networks
<p>Deep neural networks have shown promising success towards the classification and retrieval tasks for images and text data. While there have been several implementations of deep networks in the area of computer graphics, these algorithms do not translate easily across different datasets, especially for shapes used in product design and manufacturing domain. Unlike datasets used in the 3D shape classification and retrieval in the computer graphics domain, engineering level description of 3D models do not yield themselves to neat distinct classes. The current study looks at an improved form of the 3D shape deep learning algorithm for classification and retrieval through the use of techniques such as relaxed classification, use of prime angled camera angles for capturing feature detail and transfer learning for reducing the amount of data and processing time needed to train shape recognition algorithms. The proposed algorithm (MVCNN++) builds on top of multi-view convolutional neural network (MVCNN) algorithm, improving its efficacy for manufacturing part classification by enabling use of part metadata, yielding an improvement of almost 6% over the original version. With the explosive growth of 3D product models available in publicly available repositories, search and discovery of relevant models is critical to democratizing access to design models.</p>
Registration of multi-view echocardiography sequences using a subspace similarity measure
<p>Data employed for the validation of the PCA-based similarity metric proposed in "<em>Registration of multi-view echocardiography sequences using a subspace similarity measure.</em>" Peressutti <em>et al.</em> (under review).</p> <p>Data consists of echocardiography sequences of the Left ventricle of four volunteers (vol_A-vol_D) from different acoustic windows (aw_1-aw_5). The image format is metadata.<br /> For each subject, the ground-truth rigid transformations that aligns each sequence to all the others is provided. Such transformations are provided by optically tracking the position of the ultrasound imaging probe. </p> <p>For more detailed information regarding the datasets, please refer to the paper.</p> <p>Python code for testing the proposed method can be downloaded from (https://github.com/devisperessutti/Python.git), while MATLAB code can be downloaded from (https://github.com/gomezalberto/Matlab.git).</p>
VHAKG: Multi-modal Knowledge Graphs with Multi-view Videos of Daily Activities
<h2>Outline</h2> <ul> <li>This dataset is a multimodal knowledge graph (MMKG) of daily activity videos.</li> <li>This dataset integrates a KG with embedded multi-view videos created by <a href="https://github.com/aistairc/virtualhome_aist">VirtualHome-AIST</a>, an extended version of the VirtualHome simulator, and an event-centric KG generated by <a href="https://github.com/aistairc/virtualhome2kg">VirtualHome2KG</a>.</li> <li>We named this dataset <strong>VHAKG</strong> (VirtualHome-AIST-KG).</li> </ul> <h2>Details</h2> <ul> <li>VHAKG describes 2D bounding boxes of objects every five frames, compositional activities, primitive actions, target objects, object states, 3D bounding boxes, and their time-series changes.</li> <li>The videos are encoded in base64 and embedded as a literal value.</li> <li>VHAKG consists of 706 daily activity scenarios (e.g., clean desk, cook fried bread, and relax on sofa) and 3,530 videos captured by five synchronized cameras per scenario.</li> <li>The file format is <a href="https://www.w3.org/RDF/">RDF</a> (<a href="https://en.wikipedia.org/wiki/Turtle_(syntax)">Turtle</a>), which can be loaded into various <a href="https://en.wikipedia.org/wiki/Triplestore">Triplestores</a>.</li> <li>VHAKG's vocabularies are defined as an ontology and can be found in vh2kg_schema_v2.0.0.ttl.</li> </ul> <h2>Contents</h2> <ul> <li>vh2kg_video_base64.tar.gz <ul> <li><strong>{activity name}{scene}_{camera}_2dbbox.ttl</strong>: KG with video embedded in base64 format, including 2D bounding box data every 5 frames. <ul> <li>To learn more about {scene}, check <a href="https://github.com/xavierpuigf/virtualhome/tree/v2.2.0/simulation#environment">here</a>.</li> <li>To learn more about {camera}, check <a href="https://github.com/aistairc/virtualhome_unity_aist?tab=readme-ov-file#addition-of-new-four-cameras">here</a>.</li> </ul> </li> </ul> </li> <li>vh2kg_event.tar.gz <ul> <li><strong>{activity name}_{scene}.ttl</strong>: Event-centric KGs representing video content as sequences of events.</li> <li><strong>vh2kg_schema_v2.0.0.ttl</strong>: The ontology file of this dataset.</li> <li><strong>affordance.ttl</strong>: The affordance data of objects that were created by crowdsourcing. <ul> <li>Please see Section III.B of <a href="https://doi.org/10.1109/ACCESS.2023.3253807" target="_blank" rel="noopener">this paper</a> for more information.</li> </ul> </li> <li><strong>add_places.ttl</strong>: Events in which agents moved from one room to another.</li> </ul> </li> </ul> <h2>Tools</h2> <ul> <li><a href="https://github.com/aistairc/vhakg-tools">A set of tools</a> for searching and extracting videos from VHAKG is available.</li> </ul>
Multi-view spectral images
<p>Multi-view spectral images took on 07/11/2024 using Confocal 980. </p>
Shadow Neural Radiance Fields for Multi-View Satellite Photogrammetry - Dataset
<p>Data accompanying the paper Derksen, Dawa, and Dario Izzo. "Shadow Neural Radiance Fields for Multi-view Satellite Photogrammetry." <em>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition</em>. 2021.</p> <p>This dataset contains a subset of the World-View-3 images from the IEEE Data Fusion Competition 2019 - Track 3, downloaded from https://ieee-dataport.org/open-access/data-fusion-contest-2019-dfc2019.</p> <p>It is organized in four folders, one for each study area, following the original area names. Each folder contains a multi-view set of RGB images, cropped to the validation area, and rotated according to the azimuth angle. The folder also contains a Digital Surface Model of the area (DSM) as a one-band .tif file where the values contained in pixels represent the surface altitude in meters. Finally the folder contains a "metadata" file which provides for each image ID the radius (distance from satellite to scene), as well as the viewing and lighting directions (azimuth and elevation).</p> <p>The authors would like to thank the Johns Hopkins University Applied Physics Laboratory and IARPA for providing the data used in this study, and the IEEE GRSS Image Analysis and Data Fusion Technical Committee for organizing the Data Fusion Contest.</p>
RailEnV-PASMVS: a dataset for multi-view stereopsis training and reconstruction applications
<p>A Perfectly Accurate, Synthetic dataset featuring a virtual railway EnVironment for Multi-View Stereopsis (RailEnV-PASMVS) is presented, consisting of 40 scenes and 79,800 renderings together with ground truth depth maps, extrinsic and intrinsic camera parameters and binary segmentation masks of all the track components and surrounding environment. Every scene is rendered from a set of 3 cameras, each positioned relative to the track for optimal 3D reconstruction of the rail profile. The set of cameras is translated across the 100-meter length of tangent (straight) track to yield a total of 1,995 camera views. Photorealistic lighting of each of the 40 scenes is achieved with the implementation of high-definition, high dynamic range (HDR) environmental textures. Additional variation is introduced in the form of camera focal lengths, random noise for the camera location and rotation parameters and shader modifications of the rail profile. Representative track geometry data is used to generate random and unique vertical alignment data for the rail profile for every scene. This primary, synthetic dataset is augmented by a smaller image collection consisting of 320 manually annotated photographs for improved segmentation performance. The specular rail profile represents the most challenging component for MVS reconstruction algorithms, pipelines and neural network architectures, increasing the ambiguity and complexity of the data distribution. RailEnV-PASMVS represents an application specific dataset for railway engineering, against the backdrop of existing datasets available in the field of computer vision, providing the precision required for novel research applications in the field of transportation engineering.</p> <p> </p> <p><strong>File descriptions</strong></p> <ul> <li><strong>RailEnV-PASMVS.blend</strong> (227 Mb) - Blender file (developed using Blender version 2.8.1) used to generate the dataset. The Blender file packs only one of the HDR environmental textures to use as an example, along with all the other asset textures.</li> <li><strong>RailEnV-PASMVS_sample.png</strong> (28 Mb) - A visual collage of 30 scenes, illustrating the variability introduced by using different models, illumination, material properties and camera focal lengths.</li> <li><strong>geometry.zip</strong> (2 Mb) - Geometry CSV files used for scenes 01 to 20. The Bezier curve defines the geometry of the rail profile (10 mm intervals).</li> <li><strong>PhysicalDataset.7z</strong> (2.0 Gb) - A smaller, secondary dataset of 320 manually annotated photographs of railway environments; only the railway profiles are annotated.</li> <li><strong>01.7z-40.7z</strong> (2.0 Gb each) - Archive of every scene (01 through 40).</li> <li><strong>all_list.txt, training_list.txt, validation_list.txt</strong> - Text files containing the all the scene names, together with those used for validation (validation_list.txt) and training (training_list.txt), used by MVSNet.</li> <li><strong>index.csv</strong> - CSV file provides a convenient reference for all the sample files, linking the corresponding file and relative data path.</li> </ul> <p> </p> <p><strong>Steps to reproduce</strong></p> <p>The open source Blender software suite (https://www.blender.org/) was used to generate the dataset, with the entire pipeline developed using the exposed Python API interface. The camera trajectory is kept fixed for all 40 scenes, except for small perturbations introduced in the form of random noise to increase the camera variation. The camera intrinsic information was initially exported as a single CSV file (<strong>scene.csv</strong>) for every scene, from which the camera information files were generated; this includes the focal length (<strong>focalLengthmm</strong>), image sensor dimensions (<strong>pixelDimensionX</strong>, <strong>pixelDimensionY</strong>), position, coordinate vector (<strong>vectC</strong>) and rotation vector (<strong>vectR</strong>). The STL model files, as provided in this data repository, were exported directly from Blender, such that the geometry/scenes can be reproduced. The data processing below is written for a Python implementation, transforming the information from Blender's coordinate system into universal rotation (<strong>R_world2cv</strong>) and translation (<strong>T_world2cv</strong>) matrices.</p> <p> </p> <pre><code class="language-python">import numpy as np from scipy.spatial.transform import Rotation as R #The intrinsic matrix K is constructed using the following formulation: focalLengthPixel = focalLengthmm x pixelDimensionX / sensorWidthmm K = [[focalLengthPixel, 0, dimX/2], [0, focalPixel, dimY/2], [0, 0, 1]] #The rotation vector as provided by Blender was first transformed to a rotation matrix: r = R.from_euler('xyz', vectR, degrees=True) matR = r.as_matrix() #Transpose the rotation matrix, to find matrix from the WORLD to BLENDER coordinate system: R_world2bcam = np.transpose(matR) #The matrix describing the transformation from BLENDER to CV/STANDARD coordinates is: R_bcam2cv = np.array([[1, 0, 0], [0, -1, 0], [0, 0, -1]]) #Thus the representation from WORLD to CV/STANDARD coordinates is: R_world2cv = R_bcam2cv.dot(R_world2bcam) #The camera coordinate vector requires a similar transformation moving from BLENDER to WORLD coordinates: T_world2bcam = -1 * R_world2bcam.dot(vectC) T_world2cv = R_bcam2cv.dot(T_world2bcam)</code></pre> <p> </p> <p>The resulting <strong>R_world2cv</strong> and <strong>T_world2cv</strong> matrices are written to the camera information file using exactly the same format as that of <a href="https://github.com/YoYo000/BlendedMVS">BlendedMVS developed by Dr. Yao</a>. The original rotation and translation information can be found by following the process in reverse. Note that additional steps were required to convert from Blender's unique coordinate system to that of OpenCV; this ensures universal compatibility in the way that the camera intrinsic and extrinsic information is provided.</p> <p>Equivalent GPS information is provided (<strong>gps.csv</strong>), whereby the local coordinate frame is transformed into equivalent GPS information, centered around the <a href="https://www.up.ac.za/eng4">Engineering 4.0 campus, University of Pretoria</a>, South Africa. This information is embedded within the JPG files as EXIF data.</p>
MVCNN++: CAD model shape classification and retrieval using multi-view convolutional neural networks
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wikidata Dataset for Multi-View Structural Graph Summaries
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CSS3V Dataset for Multi-View Structural Graph Summaries
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MM-Office Dataset: multi-view and multi-modal dataset in an office environment
<p>MM-office is a multi-view and multi-modal dataset in an office environment (MM-Office) that records events, e.g., 'enter' to the office room, 'sit down' on the chair, and 'take out' something from a shelf, in the room assuming the daily work. These events are recorded simultaneously using eight non-directional microphones and four cameras. The audio and video clips are divided into scenes, each about 30 to 90 seconds. The amount of data was 880 clips per point and sensor. The labels available for training are given as multi-labels that indicate which each clip contains what event. Only the test data is annotated with a strong label containing the onset/offset time of each event.</p> <p>License: see the file named LICENSE.pdf</p> <p>Further information is available at [1] and Github: https://github.com/nttrd-mdlab/mm-office</p> <p>[1] Masahiro Yasuda, Yasunori Ohishi, Shoichiro Saito, Noboru Harada “Multi-view and Multi-modal Event Detection Utilizing Transformer-based Multi-sensor fusion,” in IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), 2022.</p>
Presents: a static 5x5 RGB multi-view dataset
<h1><strong>Description</strong></h1> <p>This is an RGB static multi-view test sequence captured by a Microsoft Kinect Azure DK. The captures of the sequence have been captured using a xyz actuator that allows to position the camera in a volume of 0.82x1.2x0.85 meters. It comprises 25 views arranged in a 5x5 array with a separation of 1 cm in x and y axes. The resolution of the images are 1920x1080.</p> <h2><strong>Institutions</strong></h2> <p>Research Center on Software Technologies and Multimedia Systems for Sustainability (CITSEM), Universidad Politécnica de Madrid (UPM), Madrid, Spain.</p> <p>Laboratory of Image Synthesis and Analysis, LISA department, Ecole Polytechnique de Bruxelles, Universite Libre de Bruxelles, Belgium.</p> <h2><strong>Folder structure</strong></h2> <p>The dataset is composed of:</p> <ul> <li>The calibration file: cam_params.json</li> <li>The captured views in .yuv files: 1920x1080 8bits YUV420p</li> </ul> <h1><strong>Terms of use</strong></h1> <p>Any kind of publication or report using this dataset should refer to this DOI.</p>
Deep Clustering Representation for Spatially Resolved Transcriptomics Data via Multi-view Variational Graph Auto-Encoders with Consensus Clustering
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Supplementary material 2 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
SV1: EDOF imaging : Explanation note: This video demonstrates the effect of the registered EDOF-calculation.
Supplementary material 1 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Technical information : Explanation note: Detailed technical information and additional theoretical background.
Supplementary material 3 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
SV2: Illustrative examples : Explanation note: Illustrative examples of insects and snail shell models generated with DISC3D.
Flame Tomography in a Mirror-Based Multi-View Configuration
<p>The data is the image-based tomographic reconstruction of the flame. The multiview images are obtained from the mirror and a single high-speed camera (High-Speed Vision Research Phantom UHS v1612).</p>
Spatial-MGCN: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.